Currently, in general, schemas are manually identified by humans conducting interviews with staff engineers and examining the source code, database schemas and data of the applications being integrated. It is a process that can take a long time even for highly experienced individuals. In some cases, the effort and difficulty involved is so great that the end result is not worth pursuing.
Calculation engines are typically encumbered to deal with the complexity of multiple tables in code. If a relational database is in use, it is possible to manually construct a fully linked table one column at a time by using a SQL “View” command, and specify, for each column, what the aggregation function should be. Generally speaking, the effort of putting together such SQL View commands means that only certain columns are included in the linked tables, which dramatically reduces the value of the final linked table. If a column is needed for calculations that was not included (or the wrong aggregation function was selected), the entire operation must be redone in order to bring it in to view.
Business entities often desire to quantify the costs associated with various assets owned, controlled or otherwise operated by them, including, for example collecting asset-related data pertaining to information technology (IT) assets (e.g., servers, employee computers or client systems, networking equipment, etc.).
The desire to quantify such costs may arise in connection with financial audits, accounting analysis, operation performance reviews, investment assessments, or any other asset-related analysis. One issue faced by such entities is that they suffer from a deluge of disparate systems for storing information (e.g., IT departments often control or operate large numbers of different systems, devices and software assets, with tracking information stored on a variety of platforms and systems). Consequently, when a business entity collects data relevant to these costs, such operational-related data is likely to be received from a variety of sources, and presented in different formats. When trying to process operational-related data to better quantify such costs, the schema that relates the operational-related data together becomes important.